Making AI That Can Process 1,000,000x More Data
Your AI can analyze 1GB of data. Your enterprise has 1,000,000 times that.
Let that sink in.
Every vendor demos their AI on a tidy CSV file while you're sitting on a petabyte of chaos spread across 47 different systems. ChatGPT writing Shakespeare would have an easier time if it could only see one letter at a time.
Everyone's selling you the same dream: Point AI at your data! Get insights! Disrupt! Transform! But they're building better fishing rods when you need to drain the ocean.
The 1GB Lie
Here's the dirty secret every AI vendor hopes you don't notice: their demos run on data that would fit on a USB stick from 2005.
Watch any AI product launch:
- Clean data? Check
- Single source? Check
- Under 1GB? Always
Your reality:
- 50TB in Snowflake
- 100TB in your ERP
- 200TB in unstructured documents
- That one Excel file that runs your entire supply chain
The math doesn't work. You're trying to understand Netflix by watching one frame of one movie.
The average AI sandbox handles 1-10GB. The average enterprise has 1-10 petabytes of operational data. That's a 1,000,000x gap between what AI can see and what you actually have. 99% of your data is invisible to AI right now.
The Execution Environment Nobody Wants to Talk About
Here's what every vendor conveniently forgets to mention about AI agents: they need somewhere to run. Not just compute—everyone has compute. They need an execution environment that can actually see your data. All of it. At once.
Modal built one. So did E2B. Neat little sandboxes where your AI can play with a few gigs of data, maybe run some Python, generate you a nice matplotlib chart. You might as well give Lewis Hamilton a golf cart and ask him to win the Monaco Grand Prix.
You can't fit an elephant in a shoebox. You can't analyze enterprise data in a Jupyter notebook. And you definitely can't get real insights when your AI can only see 0.0001% of your data at a time.
What 10,000 PhD Data Scientists Would Actually Do
Your board wants AI-driven insights. Your vendors sold you AI that can only see 0.0001% of your data.
See the problem?
Let me tell you what happens when a real analyst tackles an enterprise question. Not "what's our revenue?"—that's BI 101. Take a real question: "Why did our enterprise renewal rate drop 3% in accounts that upgraded their ERP systems in the last 18 months?"
First, they'd need to join data from:
- Your CRM (account data, renewal history)
- Your product database (usage metrics)
- Your support tickets (to catch ERP upgrade mentions)
- Your data warehouse (for historical patterns)
- Probably some Excel file on Janet's desktop named "FINAL_customer_segments_v2_ACTUALLY_FINAL.xlsx"
Then they'd need to understand that "renewal rate" means different things in different parts of your org. They'd need to know that your ERP data is garbage for six months after any upgrade. They'd need to remember that you changed your fiscal year in 2021 and everything before that is off by a quarter.
Your data is scattered across the digital equivalent of 68 Empire State Buildings. Good luck solving that with a chatbot.
The Bigger Box
For three years at TextQL, we've been building something everyone said was impossible: an execution environment that can hold all your data. Not samples. Not subsets. Not "connectivity" that makes you wait 45 minutes for a query to timeout. Everything, in one place, where AI can actually work with it.
Think of it this way:
- Everyone else: Giving your AI a flashlight to explore a dark warehouse
- TextQL: Turning on the stadium lights
When your AI can see everything—your Snowflake, your ERP, your CRM, that cursed SharePoint—it stops being a party trick and starts being useful. Those 10,000 PhD data scientists? They represent what happens when you give frontier models an environment where they can actually execute complex analysis without bumping into the walls of their sandbox.
We didn't solve AI. OpenAI did that. We solved the boring problem: giving AI a box big enough to hold enterprise reality.
Because when your CEO asks why you can't just "ChatGPT the data," the answer has nothing to do with AI's intelligence. Nobody built a box big enough to hold the mess.
Until now.
Every other AI company built smarter hammers. We built a bigger toolbox.
Your data doesn't fit in their sandbox. So we built something 1,000,000x bigger.
Welcome to enterprise AI that can actually see your enterprise.
You're gonna need a bigger box. We built one.